@article{MAKHILLJEAS2018131116290,
    title = {Segmentation of Brain Tumor Using K-Means Clustering Algorithm},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {13},
    number = {11},
    pages = {3942-3945},
    year = {2018},
    issn = {1816-949x},
    doi = {jeasci.2018.3942.3945},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2018.3942.3945},
    author = {D. and},
    keywords = {Magnetic Resonance Imaging (MRI),CT (Computerized Tomoghraphy) scan,image segmentation,brain tumor,k-means,thresholding},
    abstract = {Since, image segmentation is a classic inverse problem which consist of achieving a compact region
based description of the image scene by decomposing it into meaningful or spatially regions sharing similar
attributes. Tumor is nothing but uncontrolled growth of tissues in any part of the body. Tumors are of different
types and they have different treatments. The k-means algorithm is an iterative technique that is used to
partition an image into k clusters. In developed countries most research show that the number of people who
have brain tumors were died due to inaccurate detection of tumor. CT scan or MRI is directed into intracranial
cavity produces a complete image of brain and this image is visually examined for detection.}
    }